So for reference. Algeria and Morocco hate eachother. And Algeria is a Spanish ally.
So Basically now it's just standard to use mass movements of bio-trash as a geopolitical weapon.
Topology optimization? Of course you thought it would solve your problems. You fed the solver a single static load case and watched it chug on von Mises stress like it was Gatorade, all while completely ignoring principal stresses and actual mechanical properties.
Let me guess -- you just finished the SolidWorks Simulation or Autodesk Fusion generative-design marketing deck (or maybe the Ansys webinar), and now you think organic lattices are the future because the pretty red-to-blue plot said so.
You’re going to believe that right up until the first prototype hits the machine shop and they call you, asking why the flanges are thinner than the paper your college degree -- the one you Chegged your way through -- was printed on. Then the part buckles laterally under a moment that wasn’t even in your load case, crumples, and suddenly it’s your problem.
While you’re dealing with that, you’ll finally notice that the “optimized” geometry has internal corners that act as stress risers, which the solver didn’t catch because the mesh was too coarse. Whoops. Now your impossible-to-reach toolpaths are forcing the shop to either use EDM or scrap the whole batch because you gave zero thought to how the part would actually be fixtured, inspected, or survive any real manufacturing process. The QC guys aren’t even turning the CMMs on; they’re just laughing the drawings back to the office.
Then you’ll finally open Hibbeler’s Mechanics of Materials for real this time -- not just to regurgitate the von Mises formula -- and you’ll read the sections on lateral-torsional buckling and realize that the critical moment drops with the cube of the flange thickness and the square of the unbraced length. Your solver maximized both penalties while smiling at you. It never checked geometric nonlinearity. It never asked whether the material was ductile enough for the equivalent-stress assumption to hold. It never cared.
After that, you’ll crack open Timoshenko or the AISC specs, stare at the LTB equations, and it will hit you: topology optimization isn’t failing because the algorithms are bad. It’s failing because you treated a single-parameter stiffness game like it was engineering.
Anyway, Dave from a subsystem four teams away needs to move a bolt pattern over a quarter inch. Do you want to throw your CAD model away, or should I do it for you?
congratulations, you went from a part that can be laser cut and bent in 5 minutes to a part that needs to be milled from a solid block of metal. you cut 50% of the weight and 10x'd the cost.
🚨 BREAKING:
these engineers figured out how to serve Kimi K3 on @AMD MI355X at 952 tok/s/node and 118 tok/s single stream!
this crushes B200 by 3.8x in aggregate throughput/node and 1.3x in single stream decode + beats B300 on performance per dollar (48 vs 33 tok/s/$)
See how in the thread.
Bro, this is actually a huge AMD moment.
Kimi K3 is so massive that it needs 16x B200s across two NVIDIA servers, but it fits inside one 8x MI355X AMD server because AMD gives you much more HBM memory per GPU.
That single AMD node hit:
- 952 tok/s total throughput
- 118 tok/s for a single user
- Nearly 4x the throughput per node of the 16× B200 setup
- Better performance per dollar than both B200 and B300
But the craziest part is that ROCm mostly worked out of the box.
@wafer_ai only had to make a few relatively small fixes. No months of kernel engineering. No custom kernels at all.
Even the slow time-to-first-token problem came down mostly to one attention kernel not loading because Kimi had 12 heads instead of a supported shape. They simply padded 12 to 16, used AMD’s fast existing kernel, and cut cold-prefill time by roughly 2–3x
AMD’s bet on packing more HBM into each server is going to become extremely important as frontier open models keep getting larger.
If AMD keeps improving ROCm and day-one model support, data-center operators will have to seriously consider these GPUs.
The CUDA moat isn’t dead yet, but this definitely puts a big dent in it.
I find it so funny when younger developers are like "Older games cheated! It's not actually reflections, they just render the room twice!"
Yeah, and their render pipeline takes 2ms to complete and your takes 43ms.
🕐Announcement: https://t.co/jfxkwfEDb6 is now live!
A year ago, I went looking for something like a Great Replacement Tracker — a site that aggregated all the demographic data for the West and could tell me how bad things really were. To my surprise, I couldn't find one. So six months ago, I started building it myself.
Today, I'm announcing the first live version of the Great Replacement Clock. It tracks the European-descended share of the world's population and of 45 historically White countries. It consolidates historical figures and modeled projections into a single interactive timeline — sourced, auditable, and methodologically transparent.
Accurate ethnic demographic data for Western countries is uniquely difficult to access. No country directly tracks the share of its population that is of European descent. Several nations prohibit or restrict its collection outright; France's ban on ethnic statistics is the most prominent but not the only case. In other countries, official statistics obscure long-term trends through inconsistent census categories and aggregation methods that flatten meaningful distinctions. They miss and misclassify people in predictable ways: bundling Middle Eastern and North African populations into "White," absorbing later-generation immigrants into "native," or recording European immigrants to other European countries as merely "foreign." The US illustrates this: up through 2020, federal standards classified Middle Eastern and North African populations as White.
The result is a landscape in which some of the most consequential facts about the trajectory of Western societies are among the hardest to establish with precision.
This project exists to close that gap. It draws on the best available demographic evidence to reconstruct the European-descended share of each population across time, using a consistent definition across countries and eras. The sources, assumptions, and adjustments behind every figure are open to inspection.
This is a v1. If you spot a questionable figure, please flag it for review. I welcome feedback and contributions. This tool will only get better with time.
Demographic composition is a matter of public interest, not a protected secret. We deserve to know what time it is.
Chinese interview: The concept of atonement didn’t really exist in Ancient Greece and it didn’t exist in Homer’s Odyssey. It exists in your Odyssey. Did you Christianize the Odyssey?
American interview: spin the snack wheel and then read these thirst tweets they called you zaddy
How has Spain forgotten that they fought for over 700 years to expell the muslims from their lands?
Any true Spaniards should feel their blood beginning to boil. To feel the old ways stir. They need to embrace it. It isn't enough to crush the incoming horses but those who enable it as well.
You guys need to stop treating AI studio, Gemini, antigravity, deep mind, Gemma etc as different units. Because as a user, I honestly don’t care and don’t want to care, I have more important stuffs to worry about than Google’s internal org graph.
You are partnering with Gemini because maybe that’s how you operate internally but as a user I don’t need to know this.